| 1. |
- Anstee, Quentin M., et al.
(författare)
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Genome-wide association study of non-alcoholic fatty liver and steatohepatitis in a histologically-characterised cohort
- 2020
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Ingår i: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 73:3, s. 505-515
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND AND AIMS: Genetic factors associated with non-alcoholic fatty liver disease (NAFLD) remain incompletely understood. To date, most GWAS studies have adopted radiologically assessed hepatic triglyceride content as reference phenotype and so cannot address steatohepatitis or fibrosis. We describe a genome-wide association study (GWAS) encompassing the full spectrum of histologically characterized NAFLD.METHODS: The GWAS involved 1483 European NAFLD cases and 17781 genetically-matched population controls. A replication cohort of 559 NAFLD cases and 945 controls was genotyped to confirm signals showing genome-wide or close to genome-wide significance.RESULTS: Case-control analysis identified signals showing p-values ≤ 5 x 10-8 at four locations (chromosome (chr) 2 GCKR/C2ORF16; chr4 HSD17B13; chr19 TM6SF2; chr22 PNPLA3) together with two other signals with p<1 x10-7 (chr1 near LEPR and chr8 near IDO2/TC1). Case-only analysis of quantitative traits steatosis, disease activity score, NAS and fibrosis showed that the PNPLA3 signal (rs738409) was genome-wide significantly associated with steatosis, fibrosis and NAS score and identified a new signal (PYGO1 rs62021874) with close to genome-wide significance for steatosis (p=8.2 x 10-8). Subgroup case-control analysis for NASH confirmed the PNPLA3 signal. The chr1 LEPR SNP also showed genome-wide significance for this phenotype. Considering the subgroup with advanced fibrosis (≥F3), the signals on chromosomes 2, 19 and 22 remained genome-wide significant. With the exception of GCKR/C2ORF16, the genome-wide significant signals replicated.CONCLUSIONS: This study confirms PNPLA3 as a risk factor for the full histological spectrum of NAFLD at genome-wide significance levels, with important contributions from TM6SF2 and HSD17B13. PYGO1 is a novel steatosis modifier, suggesting relevance of Wnt signalling pathways in NAFLD pathogenesis.
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| 2. |
- Govaere, Olivier, et al.
(författare)
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Transcriptomic profiling across the nonalcoholic fatty liver disease spectrum reveals gene signatures for steatohepatitis and fibrosis
- 2020
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Ingår i: Science Translational Medicine. - Washington, DC, United States : American Association for the Advancement of Science (AAAS). - 1946-6234 .- 1946-6242. ; 12:572
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Tidskriftsartikel (refereegranskat)abstract
- The mechanisms that drive nonalcoholic fatty liver disease (NAFLD) remain incompletely understood. This large multicenter study characterized the transcriptional changes that occur in liver tissue across the NAFLD spectrum as disease progresses to cirrhosis to identify potential circulating markers. We performed high-throughput RNA sequencing on a discovery cohort comprising histologically characterized NAFLD samples from 206 patients. Unsupervised clustering stratified NAFLD on the basis of disease activity and fibrosis stage with differences in age, aspartate aminotransferase (AST), type 2 diabetes mellitus, and carriage of PNPLA3 rs738409, a genetic variant associated with NAFLD. Relative to early disease, we consistently identified 25 differentially expressed genes as fibrosing steatohepatitis progressed through stages F2 to F4. This 25-gene signature was independently validated by logistic modeling in a separate replication cohort (n = 175), and an integrative analysis with publicly available single-cell RNA sequencing data elucidated the likely relative contribution of specific intrahepatic cell populations. Translating these findings to the protein level, SomaScan analysis in more than 300 NAFLD serum samples confirmed that circulating concentrations of proteins AKR1B10 and GDF15 were strongly associated with disease activity and fibrosis stage. Supporting the biological plausibility of these data, in vitro functional studies determined that endoplasmic reticulum stress up-regulated expression of AKR1B10, GDF15, and PDGFA, whereas GDF15 supplementation tempered the inflammatory response in macrophages upon lipid loading and lipopolysaccharide stimulation. This study provides insights into the pathophysiology of progressive fibrosing steatohepatitis, and proof of principle that transcriptomic changes represent potentially tractable and clinically relevant markers of disease progression.
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| 3. |
- McGlinchey, Aidan J, 1984-, et al.
(författare)
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Metabolomics approaches to identify biomarkers of nonalcoholic fatty liver disease
- 2020
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Ingår i: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 73:Suppl. 1, s. S438-S438
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background and Aims: Nonalcoholic fatty liver disease (NAFLD) is a progressive liver disease that is strongly associated with type 2 diabetes. Accurate, non-invasive diagnostic tests to deliniate the different stages: degree of steatosis, grade of nonalcoholic steatohepatitis (NASH) and stage fibrosis represent an unmet medical need. In our previous studies, we successfully identified specific serum molecular lipid signatures which associate with the amount of liver fat as well as with NASH. Here we report underlying associations between clinical data, lipidomic profiles, metabolic profiles and clinical outcomes, including downstream identification of potential biomarkers for various stages of the disease.Method: We leverage several statistical and machine-learning approaches to analyse clinical, lipidomic and metabolomic profiles of individuals from the European Horizon 2020 project: Elucidating Pathways of Steatohepatitis (EPoS). We interrogate data on patients representing the full spectrum of NAFLD/NASH derived from the EPoS European NAFLD Registry (n = 627). We condense the EPoS lipidomic data into lipid clusters and subsequently apply non-rejection-rate-pruned partial correlation network techniques to facilitate network analysis between the datasets of lipidomic, metabolomic and clinical data. For biomarker identification, random forest ensemble classification and neural network machine learning approaches were used to both search for valid disease biomarkers and to assess the relative improvement over clinical-data-only classification versus addition of our lipidomic and metabolomic datasets.Results: We found that steatosis grade was strongly associated with (1) an increase of triglycerides with low carbon number and double bond count as well as (2) a decrease of specific phospholipids, including lysophosphatidylcholines. In addition to the network topology as a result itself, we also present lipid clusters (LCs) of interest to the derived network of proposed interactions in our NAFLD data from the EPoS cohort, along with our proposed biomarkers for various disease outcomes, as put forward by our current machine learning analyses.Conclusion: Our findings suggest that dysregulation of lipid metabolism in progressive stages of NAFLD is reflected in circulation and may thus hold diagnostic value as well as offer new insights about the NAFLD pathogenesis. Using this cohort as a proof-of-concept, we demonstrate current progress in tuning the accuracy of neural network and random forest approaches with a view to predicting various subtypes of NAFLD patient using a minimal set of lipidomic and metabolic markers. A detailed network-based picture emerges between lipids, polar metabolites and clinical variables. Lipidomic/metabolomic markers may provide an alternative method of NAFLD patient classification and risk stratification to guide therapy.
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| 4. |
- Sen, Parho, et al.
(författare)
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Metabolism of human liver on a genome scale in non-alcoholic fatty liver disease
- 2020
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Ingår i: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 73:Suppl. 1, s. S671-S672
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background and Aims: Non-alcoholic fatty liver disease (NAFLD) is a major risk factor leading to chronic liver disease and type 2 diabetes. By using patient-matched liver transcriptomics and serum metabolomics data from the EPoS European NAFLD Registry cohort, we conducted genome-scale metabolic modeling (GSMM) to dissect hepatic metabolism across the full spectrum of NAFLD, from steatosis (NAFL) to NASH-cirrhosis.Method: We compared the genome-scale metabolic networks across different stages of NAFLD together with healthy controls (HC, n = 10), with the patients divided into three groups: steatosis (n = 60), NASH (n = 139; F0: n = 4, F1 n = 28, F2: n = 53, F3: n = 54) and cirrhosis (n = 14). Based on transcriptomics data obtained from the liver biopsy of the patients enrolled in the European NAFLD Registry, genome-scale metabolic models of the liver were developed and contextualized for these conditions. GSMM, as a scaffold, connects metabolic genes (i.e., enzymes) and metabolic pathways. Moreover, genome-scale networks can be constrained with multi-‘omics’ datasets, and thus connect an organism’s genotype to phenotype.Results: GSMM revealed that similar metabolic functions are perturbed in NAFL and NASH, while additional metabolic processes were regulated in advanced fibrosis/cirrhosis. The primary liver processes such as glycerophospholipid metabolism, chondroitin/heparan sulfate, bile acid and fatty acid biosynthesis and oxidation (carnitine shuttle in mitochondria) were affected. Lipid precursors for VLDL particles were upregulated in NAFL. Integrative analysis of transcriptomics and serum metabolomics data also revealed that several microbial pathways are up-regulated in NAFLD and may contribute to pathogenesis.Conclusion: A GSMM approach has identified common and specific liver metabolic pathways across different stages of NAFLD progression. Data were cross-validated by serum metabolomics, where in addition analysis also revealed that specific microbially-produced metabolites are elevated in NAFLD as compared to controls. These results provide important insights into the changes in hepatic metabolism occurring during NAFLD/NASH pathogenesis.
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